With the booming development of sharing economy, decision makers must consider the effect when making decisions with uncertain demands. In the leasing problem, people are faced with several leasing ...options. Participating in the shared leasing option can reduce the cost of the lessee, which makes it a good choice. This paper considers the online leasing option under sharing economy. By applying competitive analysis to the two-option online leasing problem, the optimal competitive ratios of the deterministic and randomized strategies with market interest rate are obtained, respectively. The theoretical results show that the strategies’ competitive performance is improved under sharing economy. Furthermore, numerical examples are performed to illustrate that considering the shared option has a significant influence on the two-option online leasing problem.
The radio waves commonly used in terrestrial wireless communication are severely absorbed by the conductive seawater. However, the low-frequency magnetic fields can penetrate a much farther distance ...in seawater to realize wireless communication by magnetic induction (MI). An electromagnetic coil is generally used as a magnetic field generator, but it will consume excessive power when generating a strong magnetic field to achieve long-range communication. Instead, this study investigated the use of a motor-driven rotating permanent magnet as a mechanical transmitter for undersea MI communication. The frequency-dependent power consumption and the power-efficient operating frequency range of the mechanical transmitter were analyzed. To establish the undersea MI communication channel model, we derived exact analytic expressions for the fields generated by a rotating permanent magnet in seawater and explored the path loss of the undersea MI channel. A prototype mechanical transmitter using a cylindrical rare-earth magnet (Nd-Fe-B) with a diameter of 4 cm and a length of 15 cm driven by a servo motor was created, which consumes only about 2.74% of the power of the equivalent coil at 30 Hz. The surface-to-undersea MI communication using this prototype mechanical transmitter was demonstrated at a distance of 10 m.
In this article, we propose a hybrid approach that combines machine learning and experimental design to efficiently and accurately predict the monostatic radar cross section (RCS) of a conducting ...target versus the incident angle. The approach is called physical optics-inspired support vector regression (POI-SVR). The design of its kernel function is inspired by PO. Uniform design (UD) and uniform design sampling (UDS) are introduced to obtain highly representative training samples. Numerical experiments dealing with simple and complex targets are carried out to evaluate the accuracy and efficiency of the proposed method. The results show that our method can reduce the predictive root-mean-square error (RMSE) by 29.38%-64.78% compared with the alternative methods of combining a Gaussian SVR with the centrically located sampling (CLS), the Latin hypercube sampling (LHS), or the simple random sampling (SRS). Under the same sampling strategies (i.e., UD and UDS), POI-SVR can reduce the predictive RMSE by 11.30%-53.56% compared with the Gaussian SVR. The well-trained POI-SVR can predict the monostatic RCS of the target in any direction within 0.1 s, and in 20 000 directions within 10 s.
This paper presents a novel signal modulation method to efficiently improve the data transmission capability of the mechanical antenna based on rotating dipoles. Such mechanical antennas were shown ...to be orders of magnitude more efficient than conventional antennas in the extremely low frequency (ELF, 3-3000 Hz) band. However, the signal modulation is constrained by inertia because this mechanical antenna relies on mechanical motion rather than on electromagnetic wave resonance. When using the existing signal modulation methods designed for circuit systems, the performance of the mechanical antenna is limited. Therefore, we presented an inertial compatible modulation method for the mechanical antenna called chirp-rate shift keying (CSK). In CSK modulation, the transmitted data are directly related to the driving torque, which can change instantly regardless of inertia. The orthogonal modulation methods of CSK were also established to achieve optimal detection. To demonstrate the feasibility of CSK modulation, we conducted a wireless communication experiment that used a rotary magnet-based mechanical antenna as the transmitter. The experimental results indicated that CSK modulation requires less torque and can achieve a higher reliable communication rate at the same bit error rate (BER) level compared to conventional frequency-shift keying (FSK) modulation.
A fast adaptive surrogate modeling technique for analyzing the target's radar cross section (RCS) response versus frequency is proposed based on the Gaussian process regression (GPR). Specifically, ...an iterative process of modeling and sampling, which seeks the representative points (such as extreme points and inflection points) of the RCS curve, is presented to adaptively determine the required samples and progressively improve the modeling fidelity of the GPR. Validation experiments based on two exemplary targets are performed. Compared with the traditional GPR-based surrogate modeling technique employing a one-shot sampling strategy, the proposed adaptive GPR-based surrogate modeling technique further reduces the computational workload (more than 30%) while maintaining high accuracy.
A physics-inspired hybrid method for extrapolating the conducting target's radar cross section (RCS) versus frequency is presented. Inspired by physical optics, we propose using the nonlinear least ...squares method to capture the global trend of the conducting target's RCS and using Gaussian process regression to automatically extrapolate the remaining local fluctuations. Experiments based on simulated and measured data are carried out to verify the proposed method. This method achieves a maximum root-mean-square error of only 0.444 dBsm on the simulated data of an electrically large aircraft model, and 0.065 dBsm on the measured data of a combinatorial model. These results fully demonstrate its high extrapolation accuracy.
In this letter, we propose an improved Gaussian process regression (GPR) to accurately predict the monostatic radar cross section of conducting targets as a function of the incident angle and ...frequency. Inspired by physical optics, we assume the covariance function as the sum of linear periodic covariance functions. Experiments involving the simulated and measured data are carried out to assess the proposed method. Results show that our method has better prediction performance than GPR with a local periodic covariance function, with a consistent reduction, up to 39% on simulated data and 43% on measured data, of the predictive root mean square error.
An accurate forecast of the atmospheric refractive index structure constant (
2) is vital to analyzing the influence of atmospheric turbulence on laser transmission in advance. In this paper, we ...propose a novel method to forecast the atmospheric refractive index structure constant
2 profile, which is inspired by the turbulence characteristics (i.e., the altitude-time correlations). A deep convolutional neural network (DCNN) is adopted in the hope that with the stacked convolutional layers to abstract the altitude-time correlations of
2, it can accurately forecast the
2 profile in the near future based on the accumulated historical measurement data. While the sliding window algorithm is introduced to segment the measured time series data of the
2 profiles to generate the input-output pair data for training and testing. Experimental results demonstrate its high forecast accuracy, as the obtained root mean square error and the correlation coefficient are 0.515 and 0.956 in the one-step-ahead
2 profile forecast case, 0.753 and 0.9046 in the 36-step-ahead forecast case, respectively. Moreover, the forecast accuracy versus altitude and its relationship with the distribution of
2 against altitude are analyzed. Most importantly, with a series of experiments of various input feature sizes, the appropriate sliding window width for
2 forecast is explored, and the short-term correlation of
2 is also verified.
This paper presents a method to finely model the arbitrarily irregular-shaped and inhomogeneous dielectric target. The target is first geometrically divided into a set of homogeneous and isotropic ...tetrahedral regions. Each region is precisely matched with a set of electromagnetic parameters. As a result, this can accurately model the target which has an extremely complex dielectric constant distribution and an irregular shape. Regarding the electromagnetic scattering evaluation of the established model, the method of moments (MoM) is adopted in consideration of the coupling between these tetrahedral regions, and the total scattering is obtained by solving the matrix equation. The above two computational sections are integrated into a self-designed software. One can just input the spatial distribution of the dielectric constant and then the designed software automatically processes the target’s geometric information and meshes the target. Finally, the scattered electric field and radar cross section (RCS) of the target are output from the software. The designed software provides an effective and accurate way to study the electromagnetic scattering characteristics of the complex inhomogeneous objects.
Objective To solve the difficulty of obtaining a radar cross section (RCS) using traditional simulation and measurement methods under high frequency, this study proposes a hybrid method which ...combines bootstrap aggregation (Bagging) and spectral mixture covariance function-based Gaussian process regression (GPR) model to predict the RCS of ships in the high frequency band efficiently and accurately according to the data in the low frequency band.MethodsFirst, according to the monostatic RCS data of ships in the low frequency band, the training subset is obtained by resampling. The spectral mixture covariance function-based GPR model is then used to extrapolate the RCS data of each subset in the frequency domain. Finally, the extrapolation results of each subset are mixed by the Bagging method to further improve the extrapolation accuracy and robustness of GPR. The proposed method is then tested on the simulation data and measured data respectively. ResultsThe predicted value of the Bagging-GPR hybrid method is basically consistent with the simulated value and measured value, and the root mean square error is very small.ConclusionsThe Bagging-GPR hybrid method has high RCS extrapolation accuracy and good robustness in the frequency domain, providing a new technical means for quickly obtaining the high-frequency RCS characteristics of targets.
